…Authenticity. Having lived in different countries, it can be easy to assimilate into one’s environment. However, I’ve learned the hard way that deviating from “the real me” makes me less happy as a person, so I decided to blend in less. For me, authenticity lies in being honest and direct, favoring humor over seriousness, and striving for excellence. It also means that I appreciate other people’s character and encourage them to express their authentic selves…
Artificial Intelligence is transforming industries at a breakneck pace, and the entrepreneurs driving this innovation are at the forefront of this revolution. From groundbreaking applications to ethical considerations, these visionaries are shaping the future of AI. What does it take to innovate in such a rapidly evolving field, and how are these entrepreneurs using AI to solve real-world problems? As a part of this series, I had the pleasure of interviewing Eilon Reshef
Eilon is the co-founder and Chief Product Officer at Gong, a company using AI and machine learning to help sales and go-to-market teams win more deals and book more revenue. Eilon is a seasoned entrepreneur, creating, building and marketing software and solutions designed to solve customer challenges and deliver value.
Thank you so much for joining us in this interview series. Before we dive in, our readers would love to learn a bit more about you. Can you tell us a bit about your childhood backstory and how you grew up?
I grew up mostly in Tel Aviv, Israel, though I did spend parts of middle school in Palo Alto, which at the time — this shows how old I am — was a pretty random university town in California. I spent a big part of my teenage years programming — both as a hobby and professionally. During my high school years, I developed a software application that dynamically internationalizes various graphic software and — together with a partner — sold it commercially.
Can you share the most interesting story that happened to you since you began your career?
What’s been most interesting to see throughout my career is the evolution of AI, and specifically how AI is now integrated into the enterprise. When Gong was first founded, the term AI wasn’t considered to be a good thing for increased efficiency and productivity — as it’s most commonly known today.
It may seem ironic now that AI is so integrated into workflow processes. But when Gong was first founded, AI was deemed unpredictable and too risky, often scaring off potential investors. We had to downplay the importance of AI in Gong’s platform and shy away from the significance it would have on our future customers and the sales industry as a whole. We used terms such as “data-driven” or even “machine learning” to not scare people off. Needless to say, times have changed. We’re now presented with countless opportunities to showcase how our continued investment in AI drives actual business results for our customers.
None of us are able to achieve success without some help along the way. Is there a particular person who you are grateful for who helped get you to where you are? Can you share a story about that?
I’d have to give a large amount of credit to my co-founder, Amit. He identified the original market need to use AI to streamline revenue organizations, having been the CEO of a fast-growing company, and recruited me to join the mission to transform the way B2B sales were conducted. Eventually, we both put in the time and effort to do so.
But beyond the beginning, Amit continues to think about how to expand Gong’s footprint on the mission to be the solution that every member of the revenue organization uses to be successful.
Can you please share your favorite “Life Lesson Quote” and how it has been relevant to you in your life?
I love Hanlon’s Razor quote, which reads, “Never attribute to malice that which is adequately explained by stupidity.”
It may sound negative or even harsh, but the reality is that most people carry out actions with good intent. So rather than assuming negative intent, I instead look to see where there might be a knowledge gap or skill opportunity where I can help–obviously a much brighter side than what Hanlon called “stupidity.”
You are a successful business leader. Which three character traits do you think were most instrumental to your success? Can you please share a story or example for each?
There is more than one way to be a leader, and each person should choose the method that most aligns with their personality and values.
For me, I pay special attention to:
- Authenticity. Having lived in different countries, it can be easy to assimilate into one’s environment. However, I’ve learned the hard way that deviating from “the real me” makes me less happy as a person, so I decided to blend in less. For me, authenticity lies in being honest and direct, favoring humor over seriousness, and striving for excellence. It also means that I appreciate other people’s character and encourage them to express their authentic selves.
- Honesty and Directness. I mentioned this above, but it is also worth a separate explanation. In some cases, business culture has morphed to a point where people don’t tell things “the way they are” anymore. It’s never easy to tell the hard truth to people, and as a result, so many leaders try to avoid difficult situations. I believe that if you’re completely honest with your team — good, bad, and ugly — they appreciate it and reciprocate.
- Focusing on the Customer and the Business. While I respect people’s career aspirations, I never understood how a company can be successful if people solely focus on their personal goals instead of the company’s. I’ve always prioritized aligning with a specific company’s goals, which often are the customer’s goals as well.
Ok super. Let’s now shift to the main part of our discussion. Share the story of what inspired you to start working with AI. Was there a particular problem or opportunity that motivated you?
I studied “Machine Learning” for my Bachelor’s degree and wanted to continue down the same path for my Masters. However, similar to how AI was perceived in the enterprise a decade ago, there was very little interest in the machine learning field in the 1990s, so I had to shift gears. I began studying Deep Neural Networks when they were first released, and I even bought Nvidia stock back in 2015, right before we founded Gong.
Then, Amit, my co-founder, came to me with a real-world challenge: in his previous role as a CEO, there were up and down revenue quarters. When he tried to figure out the reason behind the down quarters, he realized that the truth lay in the communication between the customer and the salesperson (video conference, in his case). But there wasn’t a way to store and analyze these conversations at scale and in a way that would drive revenue and productivity. We thought this would be a great space to push AI into.
At the time, sellers were expected to manually enter and update CRM data gathered over a sales lifecycle. This led to an array of outdated customer data that was subject to unintentional bias and incomplete. So, we launched Gong with the idea that if we could autonomously capture all of the conversations, we could make sense of them at scale and use them to elevate what teams are doing.
We’ve now grown into a suite of revenue intelligence applications that support all stages of the revenue lifecycle, but it all started with a desire to help customer-facing teams increase revenue by efficiently leveraging their customer interactions.
Describe a moment when AI achieved something you once thought impossible. What was the breakthrough, and how did it impact your approach going forward?
The rise of LLMs was game-changing. Back in May 2021, before ChatGPT transformed the world’s perception of AI, I personally conducted a set of experiments with GPT 3 (its name at the time) and shared these experiments with my teams. This was the first time that a machine could truly transform text. I gave the LLM call transcripts and asked it to create all sorts of deliverables — like action items, customer needs, relevant participants, and more.
It was clear that this was a revolution for Gong and many other parts of the world.
Talk about a challenge you faced when working with AI. How did you overcome it, and what was the outcome?
There have been numerous challenges, especially in the early days of the pre-LLM world. LLMs tend to be powerful Swiss army knives, so you can always find out how to use them. When problems don’t fit into an LLM, things become quite complex. For example, how do you forecast a deal? Or, given thousands of accounts and deals, how do you find patterns?
In both cases and many others, we’ve developed our own proprietary algorithms, which eventually led to important customer benefits — such as better predictability or better decision-making.
Can you share an example of how your work with AI has had a meaningful impact (on others, business results, etc.)?
What started as a solution for recording and properly storing customer conversations has grown into a comprehensive revenue AI platform that drives impact across multiple trajectories. When we launched our Ask Anything capability, we started seeing sales managers spend more time self-assessing opportunities rather than spending time debriefing with individual sellers. For sellers, we’ve seen that having an AI notetaker mitigates the need to recall missing information.
For revenue organizations, the ability to build a repeatable operating rhythm for pipeline management and forecasting has increased efficiency and predictability. Our continued work with AI has allowed customers like Elsevier to close deals 35% faster, SpotOn to improve win rates by 16% and achieve 95% forecast accuracy, and Paycor to increase deal win rates by 141%.
Here is the main question for our discussion. Based on your experience and success, can you please share “Five Things You Need To Know To Help Shape The Future of AI”? (Please share a story or an example for each.)
I wouldn’t even say five. To make reasonable decisions, the first and most important thing you need to do is to get a solid understanding of what LLMs are and what they are not. Most people, even in the tech community, don’t know that, as of today, you can’t really train LLMs with “your own company data,” as technology doesn’t necessarily behave in ways that align with how we think or talk. By deeply understanding technology’s capabilities, you can more easily assess where technology is or isn’t heading and where it might be in the short and medium term.
When you think about the future of AI, what excites you the most, and how do you see your work contributing to that future?
I believe in AI’s enormous potential to create efficiencies. Today, much of the work many people do can be repetitive and procedure-based. This is the type of work that AI is primed to support.
I once tried to enumerate all of the different ways people in revenue organizations transfer knowledge, such as debrief meetings, account planning, account syncs, meeting preparation, etc. Many of those channels are designed to transfer knowledge from one head to another, but AI can do the same thing more efficiently.
In general, I believe that the media hype around digital workers is a bit exaggerated. AI will not replace us, but it can speed up many things and remove a lot of the drudgery. We believe that the future of AI is task-specific agents that are trained to perform one task really well, like briefing, reviewing deals, or training people — and we’re building those agents. But it’ll take a long time before we, and the industry as a whole, trust AI to carry out complex, ambiguous actions independently.
What advice would you give to other entrepreneurs who want to innovate in AI? Can you share a story from your experience that illustrates your advice?
The first thing is, don’t just build something that is basically an LLM with a wrapper. Anyone can do this, and eventually, they will. I believe that SaaS solves people’s or organizations’ needs and that their needs are very rarely just “AI.” So think through what the problem is, what the solution is, where AI fits in, and where you can innovate to give people and companies an end-to-end solution.
The second thing is to iterate fast. It’s hard to know what LLMs and AI can do without experimenting with them. We now know that AI can generate the next steps from a sales conversation, but can it intelligently critique a salesperson? Without trying, it’s difficult to determine the next steps for innovation or the technology’s long-term value.
How can our readers further follow your work online?
Follow me on LinkedIn and stay up to date with the Gong blog, where we aim to keep our readers informed about product updates, company milestones, and more.
This was so inspiring. Thank you so much for joining us!
Eilon Reshef Of Gong On Pushing the Boundaries of AI was originally published in Authority Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.